In 9 intensive weeks in Lausanne, learn Data Science from Python to advanced Machine Learning, get all the skills to join a Data Science team and boost your career.
Some of our upcoming courses in Lausanne can be followed remotely. After you complete your application, the admission manager will be in touch shortly to give you all the necessary information.
Our full-time Data Science in Lausanne course gives you the skills you need to launch your career in a Data Science team in only 9 weeks. From Pandas to Deep Learning, you will finish the course knowing how to explore, clean and transform data into actionable insights and how to implement Machine Learning models from start to finish in a production environment, working in teams with the best-in-class tool belt.
Our course is designed to make you learn Data Science step by step, starting with the basic data toolkit in Python and Mathematics to the complete implementation and deployment cycle of Machine Learning algorithms.
Our Data Science course is very intense. To save time and nail it from the beginning, our students must complete an online preparation work before starting the bootcamp. This work takes around 40 hours and covers the basics of Python, the pre-requisite language of the course, and some mathematical topics used every day by data scientists.
Learn programming in Python, how to work with Jupyter Notebook and to use powerful Python libraries like Pandas and NumPy to explore and analyze big data sets. Collect data from various sources, including CSV files, SQL queries on relational databases, Google Big Query, APIs and Web scraping.
Learn how to formulate a good question and how to answer it by building the right SQL query. This module will cover schema architecture and then dive deep into the advanced manipulation of SELECT to extract useful information from a stand-alone database or using a SQL client software like DBeaver.
Make your data analyses more visual and understandable by including data visualizations in your Notebook. Learn how to plot your data frames using Python libraries such as matplotlib and seaborn and transform your data into actionable insights.
Understand the underlying math behind all the libraries and models used in the bootcamp. Become comfortable with the basic concepts of statistics & probabilities (mean, variance, random variable, Bayes’s Theorem, etc.) and with matrix computation, at the core of numerical operations in libraries like Pandas and Numpy.
You'll learn how to structure a Python repository with object-oriented programming in order to clean your code and make it re-usable, how to survive the data preparation phase of a vast dataset, and how to find and interpret meaningful statistical results based on multivariate regression models
Data analysts are meant to communicate their findings to non-technical audiences: You will learn how to create impact by explaining your technical insights and turn them into business decisions using cost/benefits analysis. You'll be able to share your progress, present and compare your results to your teammates.
Learn how to explore, clean, and prepare your dataset through preprocessing techniques like vectorization. Get familiar with the classic models of supervised learning - linear and logistic regressions. Learn how to solve prediction and classification tasks with the Python library scikit-learn using learning algorithms like KNN (k-nearest neighbors).
Implement training and testing phases to make sure your model can be generalised to unseen data and deployed in production with predictable accuracy. Learn how to prevent overfitting using regularization methods and how to chose the right loss function to improve your model's accuracy.
Evaluate your model's performance by defining what to optimise and the right error metrics in order to assess your business impact. Improve your model's performance with validation methods such as cross validation or hyperparameter tuning. Finally, discover a powerful supervised learning method called SVM (Support Vector Machines).
Move to unsupervised learning and implement methods like PCA for dimensionality reduction or clustering for discovering groups in a data set. Complete your toolbelt with ensemble method that combine other models to improve performance, such as Random Forest or Gradient Boosting.
Unveil the magic behind Deep Learning by understanding the architecture of neural networks (neurons, layers, stacks) and their parameters (activations, losses, optimizers). Become autonomous to build your own networks, especially to work with images, times and text, while learning the techniques and tricks that make Deep Learning work.
Go further into computer vision with Convolutional Neural Networks, architectures designed to take the most out of images. Improve your model generalization thanks to data augmentation techniques and implement advanced methods to benefit from state-of-the-art architectures thanks to Transfer learning methods.
Get comfortable into managing sequential data and text (sequence of words) by transforming them into appropriate inputs. Leverage the power of Recurrent Neural Networks to forecast future values and perform valuable Natural Language Processing.
Discover the Keras Deep Learning library which enables to prototype easily while having the flexibility to tune precisely your neural network. Moreover, Google Colab will greatly speed up the computational time thanks to dedicated GPUs.
Move from Jupyter Notebook to a code editor and learn how to setup a machine learning project in the right way in order to quickly and confidently iterate. Learn how to convert a machine learning model into a model with a robust and scalable pipeline with sklearn-pipeline using encoders and transformers.
Building a machine learning model from start to finish requires a lot of data preparation, experimentation, iteration and tuning. We'll teach you how to do your feature engineering and hyperparameter tuning in order to build the best model. For this, we will leverage a library called MLflow.
Finally, we'll show you how to deploy your code and model to production. Using Google Cloud AI Platform, you'll be able to train your model at scale, package it and make it available to the world. Cherry on top, you will use a Docker environment to deploy your own RESTful Flask API which could be plugged to any front-end interface.
You'll spend the last two weeks on a group project working on an exciting data science problem you want to solve! As a team, you'll learn how to collaborate efficiently on a real data science project through a common Python repository and the Git flow. You will use a mix of your own datasets (if you have any from your company / non-profit organisation) and open-data repositories (Government initiatives, Kaggle, etc.). It will be a great way to practise all the tools, techniques and methodologies covered in the Data Science Course and will make you realize how autonomous you have become.
From morning lectures to evening talks, every day is action-packed.
Grab a coffee and start every morning with an engaging & interactive lecture, before putting what you’ve learnt into practice.
Pair up with your buddy for the day, and work on a series of programming challenges with the help of our teaching staff.
Learning to code is very intense, so it’s important to take a break and relax during our yoga classes, which run at least once a week.
Review the day’s challenges and get an overview of upcoming lessons during live code sessions.
Be inspired and get priceless advice from successful entrepreneurs invited for exclusive talks.
Since day one, we’ve taken teaching seriously. Great teachers inspire us to connect to topics on a profound level. Experience as a developer alone doesn’t necessarily make one an effective teacher — that’s why we’re passionate about finding not only great engineers, but deeply committed, experienced teachers.
Sinan is Product Owner for a Startup in Lausanne. He is an engineer and did the bootcamp in 2019. He keeps on developing web projects during his spare time.
more about SinanOur Data Science course is just the beginning of the journey. Once you graduate, you belong to a global tech community and have access to our online platform to keep learning and growing.
Get tips and advice from professional data scientists & data analysts, access exclusive job and freelance opportunities from entrepreneurs & developers.
Access our online education platform at any time after the course: you will find all data science lectures, screencasts, challenges and flashcards.
Benefit from our global community of 10032 alumni working in data-related roles, but also entrepreneurs, developers and product managers all over the world.
Our different courses are running in 40 campuses all over the world: wherever you go, you belong to the Le Wagon community!
Once the course ends, you benefit from our career services. We help you meet with the best recruiters and connect with relevant alumni.
Access a complete guide to kick-start your Data Science career after the course: boost your portfolio, find your dream job, leverage on our 10032 alumni community.
Attend our job fairs and networking events, meet with the best tech companies and receive offers by recruiters looking for talent in data-related roles.
Our data science course alumni love to share their experiences with fresh graduates: they explain how they found their job as Data Scientist, Data Analyst or Data Engineer.
Our local teams know their alumni and hiring partners, what they are up to and what they are looking for. They introduce you to the right people depending on your goal.
The best companies partner with Le Wagon and hire our alumni as Data Scientist, Data Analyst or Data Engineer.
Hired 6 graduates
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Hired 9 graduates
Hired 5 graduates
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Le Wagon has 1871 reviews with an average grade of 4.98/5 which makes it the most acclaimed coding bootcamp worldwide on Switchup according to student reviews! Whilst these reviews make us proud, above all they make us happy. Behind these reviews are hundreds of people around the world who have had a life-changing experience that has opened new doors for them. We feel blessed and honoured to have enabled them - and to keep enabling our students - to become autonomous in coding, upskill or change their career, or launch their own startups.
Also, these 1871 reviews are extremely important for us to make sure we’re always meeting the same level of excellence. Having such positive and enthusiastic reviews about our bootcamps is the ultimate proof that we provide the best tech education for all our students, in the 40 cities we run our coding bootcamps in.
If you got here, it means that you have questions about Le Wagon: how to apply, who can join, what you’ll learn… Good! We have the answers.
[Full-time] In Lausanne, we currently teach in French. However, we have requests to do batches in English. If you are interested by a batch in English, please voice your interest here: Quick survey for batches in English in Lausanne
[Part-time] The Part-time bootcamp is opened to both French and English speakers. The videos of the lectures are available in FR & EN. Then, during the exercises phases, teachers help students in their mother tongue.
Yoga lessons are not set yet in Lausanne. However, we advise students to take breaks regularly because days are intense 😅
Once a week on average, an event is hosted at Le Wagon, and students are more than welcome to join. It usually takes place from 18.30 to 20.00 and it is a good opportunity to meet people working in Tech locally.
Find all our events here
[Full-time] A typical day starts at 8.30 and ends at 18.00. There is a lecture from 8.30 to 9.30, followed by exercises or projects, and a live-coding session from 16.30 until 18.00.
[Part-time] Please check the schedule here
For both the Full-time & Part-time formats, lectures are maintained during bank holidays, unless stated in the training contract. There is a 3 weeks break during Xmas holidays for the Part-time.
[Full-time] The payment is made in 2 installments: 50% before starting the bootcamp and 50% around the middle.
[Part-time] The payment is made in 3 installments: 30% before starting the bootcamp, 40% around the middle and 30% just before the end.
Yes, a fair minimum. Our Data Science bootcamp is very intense and will jump into advanced topics from the very first week. Unlike our FullStack Web Development bootcamp where we spend 3 weeks learning Ruby from scratch, applicants for the Data Science bootcamp need some programming. How much? You must be comfortable with data types & variables, conditions, loops, functions and the two data structures list & dict (named Array and Hash in Ruby). If you know those topics in Ruby, JavaScript or some other language than Python, feel free to embark on those free Fundamentals & Intermediate courses before the bootcamp.
A minimum math level is required for the bootcamp. We need you to be comfortable with Functions, their Derivatives & System of linear equations. To get up to speed, some preparation work is given to applicants before the bootcamp start: Calculus, Linear Algebra & Statistics.
[Full-time] We are lucky to have a great restaurant in the building that serves 2 different menus each day made with local and fresh products. Price is 13.50 CHF for a menu.
Some students prefer to bring their own food and use the small kitchen area at Le Wagon (fridge and micro-wave). There are also supermarkets around.
[Part-time] We advise students to eat before/after the evening session or to bring something to eat with them.
We can arrange a room with a host family or in an apartment close to Le Wagon for about 600 CHF / month. It is better to ask in advance to secure accommodation.
🚃 By train, the easiest is to stop at Renens station and take the bus (5 min) or walk (10 min) to Le Wagon. It is also possible to stop at Malley station.
🚎 By bus, lines 17 & 19 will drop you right at Le Wagon. Lines 32 & 33 also stop closeby.
🚗 By car, we can arrange parking spots. Spots cost 150 CHF for the bootcamp. It is better to ask in advance to secure a spot.
Le Wagon Lausanne on Google Maps
Our batches average 12 students in Lausanne. It is a great opportunity to benefit from personalized teaching and tight relationship with other students. This number keeps growing batch after batch.
Le Wagon delivers a certificate of completion for the students who followed the bootcamp seriously and completed the daily exercises and projects.
Le Wagon in Lausanne is certified by Eduqua, a high quality label for schools in Switzerland (Certificate #18688-43). However, the Le Wagon certificate is not yet recognized officially by the Swiss authorities.
We always advise our students to study in the city where they plan to settle down in the future.
At Le Wagon, you will learn new skills, but you will also develop your personal network, with other students and alumni. You will also be exposed to the local Tech ecosystem and benefit from the links built between Le Wagon and local companies, startups, incubators, etc...
Next Lausanne Data Science course (full-time coding bootcamp) starts on Apr 26, 2021
